Nanoengineered multimodal contrast agent for medical image guidance
Bibliographic record
Abstract
Multimodality imaging has gained momentum in radiation therapy planning and image-guided treatment delivery. Specifically, computed tomography (CT) and magnetic resonance (MR) imaging are two complementary imaging modalities often utilized in radiation therapy for visualization of anatomical structures for tumour delineation and accurate registration of image data sets for volumetric dose calculation. The development of a multimodal contrast agent for CT and MR with prolonged in vivo residence time would provide long-lasting spatial and temporal correspondence of the anatomical features of interest, and therefore facilitate multimodal image registration, treatment planning and delivery. The multimodal contrast agent investigated consists of nano-sized stealth liposomes encapsulating conventional iodine and gadolinium-based contrast agents. The average loading achieved was 33.5 ± 7.1 mg/mL of iodine for iohexol and 9.8 ± 2.0 mg/mL of gadolinium for gadoteridol. The average liposome diameter was 46.2 ± 13.5 nm. The system was found to be stable in physiological buffer over a 15-day period, releasing 11.9 ± 1.1% and 11.2 ± 0.9% of the total amounts of iohexol and gadoteridol loaded, respectively. 200 minutes following in vivo administration, the contrast agent maintained a relative contrast enhancement of 81.4 ± 13.05 differential Hounsfield units (ΔHU) in CT (40% decrease from the peak signal value achieved 3 minutes post-injection) and 731.9 ± 144.2 differential signal intensity (ΔSI) in MR (46% decrease from the peak signal value achieved 3 minutes post-injection) in the blood (aorta), a relative contrast enhancement of 38.0 ± 5.1 ΔHU (42% decrease from the peak signal value achieved 3 minutes post-injection) and 178.6 ± 41.4 ΔSI (62% decrease from the peak signal value achieved 3 minutes post-injection) in the liver (parenchyma), a relative contrast enhancement of 9.1 ± 1.7 ΔHU (94% decrease from the peak signal value achieved 3 minutes post-injection) and 461.7 ± 78.1 ΔSI (60% decrease from the peak signal value achieved 5 minutes post-injection) in the kidney (cortex) of a New Zealand white rabbit. This multimodal contrast agent, with prolonged in vivo residence time and imaging efficacy, has the potential to bring about improvements in the fields of medical imaging and radiation therapy, particularly for image registration and guidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".